What Does It Mean to ‘Solve’ the Problem of Discrimination in Hiring?

Abstract

Discriminatory practices in recruitment and hiring are an ongoing issue that is of concern not just for workplace relations, but also for wider understandings of economic justice and inequality. Yet the way decisions are made on who is eligible for jobs, and why, are rapidly changing with the advent and growth in uptake of automated hiring systems (AHSs) powered by data-driven tools .A recent report estimated that 98% of Fortune 500 companies use Applicant Tracking Systems of some kind in their hiring process. Several of these AHSs claim to detect and mitigate discriminatory practices against protected groups. Yet whilst these tools have a growing user-base around the world, such claims of ‘bias mitigation’ are rarely scrutinised and evaluated, and when done so, have almost exclusively been from a US social and legal perspective.

In this paper, we introduce a perspective from outside the US by critically examining how three prominent automated hiring systems (AHSs) in regular use in the UK, HireVue, Pymetrics and Applied, understand and attempt to mitigate bias and discrimination. These systems have been chosen as they explicitly claim to address issues of discrimination in hiring and provide some information about how their systems work to do this. Using publicly available documents, we describe how their tools are designed, validated and audited for bias, highlighting assumptions and limitations, before situating these in the social and legal context of the UK. The UK has a very different legal background to the US in terms not only of hiring and equality law, but also in terms of data protection (DP) law. We argue that this might be an important challenge to building bias mitigation into AHSs definitively capable of meeting EU legal standards. Furthermore attempts at bias mitigation intended to meet US law may not map to UK or EU law. AHSs may thus obscure rather than improve systemic discrimination in the workplace.

Keywords: hiring, employment, automated systems, hiring algorithms, fairness, bias, law, UK, mitigation